17/9/20: Exploding errors: COVID19 and VUCA world of economic growth forecasts

 

Just as I covered the latest changes in Eurozone growth indicators (https://trueeconomics.blogspot.com/2020/09/17920-eurocoin-leading-growth-indicator.html), it is worth noting the absolutely massive explosion in forecast errors triggered by the VUCA environment around COVID19 pandemic.

My past and current students know that I am a big fan of looking at risk analysis frameworks from the point of view of their incompleteness, as they exclude environments of deeper uncertainty, complexity and ambiguity in which we live in the real world. Well, here is a good illustration:

You can see an absolute explosion in the error term for growth forecasts vs actual outrun in the three quarters of 2020 so far. The errors are off-the-scale compared to what we witnessed in prior recessions/crises. 

This highlights the fact that during periods of elevated deeper uncertainty, any and all forecasting models run into the technical problem of risk (probabilities and impact assessments) not being representative of the true underlying environment with which we are forced to work.  

25/7/20: Markets and COVID19: Unwinding Restrictions

My column for Manning Financial on the direction of the markets in the period of COVID19 restrictions unwinding: https://cfc.ie/2020/07/10/where-to-mr-market/.

23/7/20: Globalization and Populism: A Recent Study

I recently came across a fascinating paper by Dani Rodrik, an economist always worth reading. The paper, titled "Why Does Globalization Fuel Populism? Economics, Culture, and the Rise of Right-wing Populism" (NBER Working Paper No. 27526, July 2020) argues that "there is compelling evidence that globalization shocks, often working through culture and identity, have played an important role in driving up support for populist movements, particularly of the right-wing kind."

Rodrik carries out "an empirical analysis of the 2016 presidential election in the U.S. to show globalization-related attitudinal variables were important correlates of the switch to Trump."

• "Trump voters were more likely to be white, older, and college-educated. 
• "...they were significantly more hostile to racial equality and perceived themselves to be of higher social class. 
• "The estimated coefficient on racial attitudes is particularly large: a one-point increase in the index of racial hostility – which theoretically ranges from 1 to 5 – is associated with a 0.28 percentage point increase in the probability of voting for Trump (Table below, column 1). 
• "By contrast, economic insecurity does not seem to be associated with a propensity to vote for Trump.

"The finding that Trump voters thought of themselves as belonging to upper social classes ... largely reflects the role played by party identification in shaping voting preferences. When we control for Republican party identification (cols. 2 and 6), the estimated coefficient for social class drops sharply and ceases to be statistically significant."

"Note, however, that racial hostility remains significant, although its estimated coefficient becomes smaller (cols. 2 and 6)."

The other columns in the table above examine attitudes towards globalization (columns 2-5).

• "All three of our measures enter statistically significantly: 
• "Trump voters disliked trade agreements and immigration; 
• "They were also against bank regulation (presumably in line with the general anti-regulation views of (cols. 2-5) the Republican party). 
• "These indictors remain significant in the kitchen-sink version where they are all entered together (col. 6)."

"In none of these regressions does economic insecurity (financial worries) enter significantly. This
changes when we move from Trump voters in general to switchers from Obama to Trump (cols. 7-12). ... financial worries now becomes statistically significant, and switchers do not identify with the upper social classes. "

"Switchers are similar to Trump voters insofar as they too dislike trade agreements and immigration
(cols. 9-11). But they are dissimilar in that they view regulation of banks favorably. Hence switchers
appear to be against all aspects of globalization – trade, immigration, finance. the regression."


Rodrik postulates "a conceptual framework to clarify the various channels through which globalization can stimulate populism" on both "the demand and supply sides of politics". He also lists "the different causal pathways that link globalization shocks to political outcomes". 

Rodrik identifies "four mechanisms in particular, two each on the demand and supply sides:

• (a) a direct effect from economic dislocation to demands for anti-elite, redistributive policies; 
• (b) an indirect demand-side effect, through the amplification of cultural and identity divisions; 
• (c) a supply-side effect through political candidates adopting more populist platforms in response to economic shocks; and 
• (d) another supply-side effect through political candidates adopting platforms that deliberately inflame cultural and identity tensions in order to shift voters’ attention away from economic issues."

The full paper, accessible at https://www.nber.org/papers/w27526.pdf is choke full of other insights and is absolutely worth reading.

30/4/20: No, Healthcare Systems are Not Lean Startups, Mr. Musk

A tweet from @elonmusk yesterday has prompted a brief response from myself:

https://twitter.com/GTCost/status/1255681426445365248?s=20

For two reasons, as follows, it is worth elaborating on my argument a little more:

1. I have seen similar sentiment toward authorities' over-providing healthcare system capacity in other countries as well, including, for example in Ireland, where the public has raised some concerns with the State contracting private hospitals for surplus capacity; and
2. Quite a few people have engaged with my response to Musk.

So here are some more thoughts on the subject:

'Lean startups' is an idea that goes hand-in-hand with the notion that a startup needs some organic growth runway. In other words, it needs to ‘nail’ parts of its business model first, before ‘scaling’ the model up. ‘Nailing’ bit is done using highly scarce resources pre-extensive funding (which is a ‘scaling’ phase). It makes perfect sense for a start up, imo, for a startup.

But in the ‘nailing’ stage, when financial resources are scarce, the startup enterprise has another resource is relies upon to execute on a ‘lean’ strategy: time. Why? Because a ‘lean’ startup is a smaller undertaking than a scaling startup. As a result, failure at that stage carries lower costs. In other words, you can be ‘lean’ because you are allowed to fail, because if you do fail in that stage of development, you can re-group and re-launch. You can afford to be reactive to news flows and changes in your environment, which means you do not need to over-provide resources in being predictive or pro-active. Your startup can survive on lean funding.

As you scale startup, you accumulate resources (investment and retained earnings) forward. In other words, you are securing your organization by over-providing capacity. Why? Because failure is more expensive for a scaling startup than for a 'lean' early stage startup. The notion of retained and untilized cash is no longer the idea of waste, but, rather a prudential cushion. Tesla, Mr. Musk's company, carries cash reserves and lines of credit that it is NOT using at the moment in time precisely because not doing so risks smaller shocks to the company immediately escalating into existential shocks. And a failure of Tesla has larger impact than a failure of small 'lean' startup. In other words, Mr. Musk does not run a 'lean startup' for a good reason. Now, in a public health emergency with rapid rates of evolution and high degree of forecast uncertainty, you cannot be reactive. You must allocate resources to be pro-active, or anticipatory. In doing so, you do not have a choice, but to over-supply resources. You cannot be ‘lean’, because the potential (and highly probable) impact of any resource under-provision is a public health threat spinning out of control into a public health emergency and a systemic shock. ‘Lean’ startup methods work, when you are dealing with risk and uncertainty in a de-coupled systems with a limited degree of complexity involved and the range of shocks impact limited by the size of the organization/system being shocked. Public health emergence are the exact opposite of such a environment: we are dealing with severe uncertainty (as opposed to risk) with hugely substantial impacts of these shocks (think thousands of lives here, vs few million dollars in investment in an early stage start up failure). We are also dealing with severe extent of complexity. High speed of evolution of threats and shocks, uncertain and potentially ambiguous pathways for shocks propagation, and highly complex shock contagion pathways that go beyond the already hard-to-model disease contagion pathways. So a proper response to a pandemic, like the one we are witnessing today, is to use an extremely precautionary principle in providing resources and imposing controls. This means: (1) over-providing resources before they become needed (which, by definition, means having excess capacity ex-post shock realization); (2) over-imposing controls to create breaks on shock contagion (which, by definition, means doing too-much-tightening in social and economic environment), (3) doing (1) and (2) earlier in the threat evolution process rather than later (which means overpaying severely for spare capacity and controls, including - by design - at the time when these costs may appear irrational). And (4), relying on the worst-case-scenario parameterization of adverse impact in your probabilistic and forecasting analysis and planning. This basis for a public health threat means that responses to public health threat are the exact opposite to a ‘lean’ start up environment. In fact they are not comparable to the ‘scaling up’ start up environment either. A system that has a huge surplus capacity left in it, not utilized, in a case of a start up is equivalent to waste. Such system’s leadership should be penalized. A system that has a huge surplus capacity left un-utilized, in a case of a pandemic is equivalent to the best possible practice in prudential management of the public health threat. Such system’s leadership should be applauded.

And even more so in the case of COVID pandemic. Mr. Musk implies something being wrong with California secured hospital beds capacity running at more than double the rate of COVID patients arrivals. That's the great news, folks. COVID pandemic carries infection detection rates that double the population of infected individuals every 3-30 days, depending on the stage of contagion evolution. Earlier on, doubling times are closer to 3 days, later on, they are closer to 30 days. But, utilization of hospital beds follows an even more complex dynamic, because in addition to the arrival rates of new patients, you also need to account for the duration of hospital stay for patients arriving at different times in the pandemic. Let's be generous to sceptics, like Mr. Musk, and assume that duration-of-stay adjusted arrivals of new patients into the hospitals has a doubling time of the mid-point of 3-30 days or, close to two weeks. If California Government did NOT secure massively excessive capacity for COVID patients in advance of their arrival, the system would not have been able to add new capacity amidst the pandemic on time to match the doubling of new cases arrivals. This would have meant that some patients would be able to access beds only later in the disease progression period, arriving to hospital beds later in time, with more severe impact from the disease and in the need of longer stays and more aggressive interventions. The result would have been even faster doubling rate in the demand for hospital beds with a lag of few days. You can see how the system shortages would escalate out of control.

Running tight supply chains in a pandemic is the exact opposite to what has to be done. Running supply capacity at more than double the rate of realized demand is exactly what needs to be done. We do not cut corners on basic safety equipment. Boeing did, with 737-Max, and we know where they should be because of this. We most certainly should not treat public health pandemic as the basis for cutting surplus safety capacity in the system.

20/3/20: Euromoney on Risk Landscape Changes

Euromoney on changing risk landscape for global sovereigns: https://www.euromoney.com/article/b1ktp0wqc12jyb/ecr-risk-experts-contemplate-another-financial-crisis. With a comment from myself.

21/1/20: Investor Fear and Uncertainty in Cryptocurrencies

Our paper on behavioral biases in cryptocurrencies trading is now published by the Journal of Behavioral and Experimental Finance volume 25, 2020:

We cover investor sentiment effects on pricing processes of 10 largest (by market capitalization) crypto-currencies, showing direct but non-linear impact of herding and anchoring biases in investor behavior. We also show that these biases are themselves anchored to the specific trends/direction of price movements. Our results provide direct links between investors' sentiment toward:

1. Overall risky assets investment markets,
2. Cryptocurrencies investment markets, and
3. Macroeconomic conditions,

and market price dynamics for crypto-assets. We also show direct evidence that both markets uncertainty and investor fear sentiment drive price processes for crypto-assets.

27/2/18: Volatility & Uncertainty are Back with a Bang

My recent article on markets volatility for the Sunday Business Post https://www.businesspost.ie/business/volatility-uncertainty-back-bang-408742

18/11/17: North Korean Uncertainty and Market Impacts

S&P new post about the risks poised by North Korea is a neat summary of key actions and players involved (see the full note: https://marketintelligence.spglobal.com/blog/global-credit-risk-spikes-as-key-apac-countries-respond-to-the-north-korean-threat).

And it is very interesting to those of us, who study the links between geopolitical risks and financial markets.

Two pieces of evidence are presented in the S&P note worth pondering: first, the rising frequency of the North Korea threat signals:

The above shows that starting with 2016, acceleration in the North Korea threat signals has been posing a departure from the previous trend. Structurally, this suggests that we are entering a new regime in terms of potential market spillovers from North Korean risks to global financial markets.

Next, some evidence on changes in specific shares valuations timed close to the North Korean threat signals:

The evidence above suggests that, in line with our research findings in other instances, the uncertainty about North Korean threat evolution is feeding into the valuations of defence stocks. And that this effect is still ambiguous. Which is in line with our findings on the links between actual conflicts and defence stocks valuations revealed in my paper with Mulhair, Andrew, "Performance Analysis of U.S. Defense Stocks in Relation to Federal Budgets and Military Conflicts in the Post-Cold War Era" (April 2017). Available at SSRN: https://ssrn.com/abstract=2975368. Furthermore, the nature of North Korean policy-induced uncertainty is consistent with our findings relating to terrorism spillovers to financial markets as revealed in my paper with Corbet, Shaen and Meegan, Andrew, "Long-Term Stock Market Volatility and the Influence of Terrorist Attacks in Europe" (August 2017). Available at SSRN: https://ssrn.com/abstract=3033951. Note, the latter paper is now forthcoming in the Quarterly Review of Economics and Finance.

While explicit testing of spillovers from North Korean uncertainty to global financial markets is yet to be firmly established in empirical literature, it is worth noting that the indirect evidence (based on data similar to S&P blog post) suggests that North Korean threat is likely to have a significant VUCA-consistent effect on the markets.

28/10/17: Income Inequality: Millennials vs Baby Boomers

OECD's recent report, "Preventing Ageing Unequally", has a wealth of data and analysis relating to old-age poverty and demographic dynamics in terms of poverty evolution. One striking chart from the report shows changes in income inequality across two key demographic cohorts: the Baby Boomers (born at the start of the second half of the 20th Century) and the Millennials (born in the last two decades of the 20th Century):

Source: http://www.oecd.org/employment/preventing-ageing-unequally-9789264279087-en.htm.

The differences between two generations, controlling for age, are striking. In my opinion, the dramatic increase in income inequality across two generations in the majority of OECD economies (caveats to Ireland and Greece dynamics, and a major outliers of Switzerland, France and the Netherlands aside) is one of the core drivers for changing perceptions of the legitimacy of the democratic ethics and values when it comes to public perceptions of democracy. 

You can read more on the latter set of issues in our recent paper, here: http://trueeconomics.blogspot.com/2017/09/7917-millennials-support-for-liberal.html.

The dynamics of income inequality for the Millennials do not appear to relate to unemployment, but rather to the job markets outcomes (which seemingly are becoming more polarized between high quality jobs/careers and low quality ones):

In other words, where as in the 1950s it was sufficient to have a job to gain a place on a social progression ladder, today younger workers need to have the job (at Google, or Goldman Sachs, or other 'star' employers) to achieve the same.

Thus, as low unemployment swept across the advanced economies in the post-Global Financial Crisis recovery, there has not been a symmetric amelioration of the youth poverty rates in a number of countries:

In 25 OECD countries out of 35, poverty rates for those aged 18-25 are today higher than for those of age 65-75. Across the OECD, statistically, poverty rates for the 18-25 year olds cohort are on par with those for of 76+ year olds cohort, and both are above 12 percent. 

There is a lot that is still missing in the above comparatives. For example, the above numbers do not adjust for differences between different age groups in terms of quality of health and education. Younger workers are also healthier, as a cohort, than older population groups. This means that their incomes should be expected to be higher than older workers, simply by virtue of better health.  Younger workers are also better educated than their older counterparts, especially if we consider the same age cohorts for current Millennials and the Baby Boomers. Which also implies that their incomes should be higher and their income inequality should be lower than that for the Baby Boomers.

In other words, simple comparatives under-estimate the extent of income inequality and poverty incidence and depth for the Millennials by excluding adjustments for health and education differences.

3/10/17: Ambiguity Fun: Perceptions of Rationality?

Here is a very insightful and worth studying set of plots showing the perceived range of probabilities under subjective measure scenarios. Source: https://github.com/zonination/perceptions

The charts above speak volumes about both, our (human) behavioural biases in assessing probabilities of events and the nature of subjective distributions.

First on the former. As our students (in all of my courses, from Introductory Statistics, to Business Economics, to advanced courses of Behavioural Finance and Economics, Investment Analysis and Risk & Resilience) would have learned (to a varying degree of insight and complexity), the world of Rational expectations relies (amongst other assumptions) on the assumption that we, as decision-makers, are capable of perfectly assessing true probabilities of uncertain outcomes. And as we all have learned in these classes, we are not capable of doing this, in part due to informational asymmetries, in part due to behavioural biases and so on. 

The charts above clearly show this. There is a general trend in people assigning increasingly lower probabilities to less likely events, and increasingly larger probabilities to more likely ones. So far, good news for rationality. The range (spread) of assignments also becomes narrower as we move to the tails (lower and higher probabilities assigned), so the degree of confidence in assessment increases. Which is also good news for rationality. 

But at that, evidence of rationality falls. 

Firstly, note the S-shaped nature of distributions from higher assigned probabilities to lower. Clearly, our perceptions of probability are non-linear, with decline in the rate of likelihoods assignments being steeper in the middle of perceptions of probabilities than in the extremes. This is inconsistent with rationality, which implies linear trend. 

Secondly, there is a notable kick-back in the Assigned Probability distribution for Highly Unlikely and Chances Are Slight types of perceptions. This can be due to ambiguity in wording of these perceptions (order can be viewed differently, with Highly Unlikely being precedent to Almost No Chance ordering and Chances Are Slight being precedent to Highly Unlikely. Still, there is a lot of oscillations in other ordering pairs (e.g. Unlikely —> Probably Not —> Little Chance; and We Believe —> Probably. This also consistent with ambiguity - which is a violation of rationality.

Thirdly, not a single distribution of assigned probabilities by perception follows a bell-shaped ‘normal’ curve. Not for a single category of perceptions. All distributions are skewed, almost all have extreme value ‘bubbles’, majority have multiple local modes etc. This is yet another piece of evidence against rational expectations.

There are severe outliers in all perceptions categories. Some (e.g. in the case of ‘Probably Not’ category appear to be largely due to errors that can be induced by ambiguous ranking of the category or due to judgement errors. Others, e.g. in the case of “We Doubt” category appear to be systemic and influential. Dispersion of assignments seems to be following the ambiguity pattern, with higher ambiguity (tails) categories inducing greater dispersion. But, interestingly, there also appears to be stronger ambiguity in the lower range of perceptions (from “We Doubt” to “Highly Unlikely”) than in the upper range. This can be ‘natural’ or ‘rational’ if we think that less likely event signifier is more ambiguous. But the same holds for more likely events too (see range from “We Believe” to “Likely” and “Highly Likely”).

There are many more points worth discussing in the context of this exercise. But on the net, the data suggests that the rational expectations view of our ability to assess true probabilities of uncertain outcomes is faulty not only at the level of the tail events that are patently identifiable as ‘unlikely’, but also in the range of tail events that should be ‘nearly certain’. In other words, ambiguity is tangible in our decision making. 

Note: it is also worth noting that the above evidence suggests that we tend to treat inversely certainty (tails) and uncertainty (centre of perceptions and assignment choices) to what can be expected under rational expectations:

In rational setting, perceptions that carry indeterminate outruns should have greater dispersion of values for assigned probabilities: if something is is "almost evenly" distributed, it should be harder for us to form a consistent judgement as to how probable such an outrun can be. Especially compared to something that is either "highly unlikely" (aka, quite certain not to occur) and something that is "highly likely" (aka, quite certain to occur). The data above suggests the opposite.

28/7/17: Risk, Uncertainty and Markets

I have warned about the asymmetric relationship between markets volatility and leverage inherent in lower volatility targeting strategies, such as risk-parity, CTAs, etc for some years now, including in 2015 posting for GoldCore (here: http://www.goldcore.com/us/gold-blog/goldcore-quarterly-review-by-dr-constantin-gurdgiev/). And recently, JPMorgan research came out with a more dire warning:

This is apt and timely, especially because volatility (implied - VIX, realized - actual bi-directional or semi-var based) and uncertainty (implied metrics and tail events frequencies) have been traveling in the opposite direction  for some time.

Which means (1) increasing (trend) uncertainty is coinciding with decreasing implied risks perceptions in the markets.

Meanwhile, markets indices are co-trending with uncertainty:

Which means (2) increasing markets valuations are underpricing uncertainty, while focusing on decreasing risk perceptions.

In other words, both barrels of the proverbial gun are now loaded, when it comes to anyone exposed to leverage.

7/6/17: European Policy Uncertainty: Still Above Pre-Crisis Averages

As noted in the previous post, covering the topic of continued mis-pricing by equity markets of policy uncertainties, much of the decline in the Global Economic Policy Uncertainty Index has been accounted for by a drop in European countries’ EPUIs. Here are some details:

In May 2017, EPU indices for France, Germany, Spain and the UK have dropped significantly, primarily on the news relating to French elections and the moderation in Brexit discussions (displaced, temporarily, by the domestic election). Further moderation was probably due to elevated level of news traffic relating to President Trump’s NATO visit. Italy’s index rose marginally.

Overall, European Index was down at 161.6 at the end of May, showing a significant drop from April 252.9 reading and down on cycle high of 393.0 recorded in November 2016. The index is now well below longer-term cycle trend line (chart below). 

However, latest drop is confirming overall extreme degree of uncertainty volatility over the last 18 months, and thus remains insufficient to reverse the upward trend in the ‘fourth’ regime period (chart below).

Despite post-election moderation, France continues to lead EPUI to the upside, while Germany and Italy remain two drivers of policy uncertainty moderation. This is confirmed by the period averages chart below:

Overall, levels of European policy uncertainty remain well-above pre-2009 averages, even following the latest index moderation.

7/6/17: Equity Markets Continue to Mis-price Policy Risks

There has been some moderation in the overall levels of Economic Policy Uncertainty, globally, over the course of May. The decline was primarily driven by European Uncertainty index falling toward longer-term average (see later post) and brings overall Global EPU Index in line with longer term trend (upward sloping):

This meant that short-term correlation between VIX and Global EPUI remained in positive territory for the second month in a row, breaking negative correlations trend established from October 2015 on.

The trends in underlying volatility of both VIS and Global EPUI remained largely the same:

The key to the above data is that equity markets risk perceptions remain divorced from political risks and uncertainties reflected in the Global EPUI. This is even more apparent when we consider actual equity indices as done below:

Both, on longer-run trend comparative and on shorter term level analysis bases, both S&P 500 and NASDAQ Composite react in the exactly opposite direction to Global Economic Policy Uncertainty measure: rising uncertainty in the longer run is correlated with rising equities valuations.

28/4/17: The VUCA Markets

My regular column with the Cayman Financial Review covering the current development of the risk-uncertainty frameworks in the markets is now available here: http://www.caymanfinancialreview.com/2017/04/26/welcome-to-the-vuca-world/.

28/2/17: Sentix Euro Breakup Contagion Risk Index Explodes

Sentix Euro Break-up Contagion Index - a market measure of the contagion risk from one or more countries leaving the euro area within the next 12 months period - has hit its post-2012 record recently, reaching 47.6 marker, up on 25 trough in 2Q 2016:

Key drivers: Greece, Italy and France.

Details here: https://www.sentix.de/index.php/sentix-Euro-Break-up-Index-News/euro-break-up-index-die-gefaehrlichen-drei.html.

23/2/17: Welcome to the VUCA World

Much has been said recently about the collapse of ‘risk gauges’ in the financial markets, especially on foot of the historically low readings for the markets’ ‘fear index’, VIX. In terms of medium-term averages, current VIX readings are closely matching the readings for the period of ‘peak’ ‘Great Moderation’ of 1Q 2005 - 4Q 2006, while on-trend, VIX is currently running below 2005-2006 troughs. In other words, risk has effectively disappeared from the investors’ (or rather traders and active managers) radars (see chart below).

At the same time, traditional perceptions of risk in the financial markets have been replaced by a sky-rocketing uncertainty surrounding the real economy, and especially, economic policies. The Economic Policy Uncertainty Indices have been hitting all-time highs globally (see chart below), and across a range of key economies (see this for my recent analysis for Europe: http://trueeconomics.blogspot.com/2017/01/15117-2016-was-year-of-records-breaking.html, this for Russia and the U.S.: http://trueeconomics.blogspot.com/2017/01/17117-russian-economic-policy.html). In current data, Economic Policy Uncertainty Index (EPUI) has been showing extreme volatility coupled with extreme valuations. Index values are rising above historical norms both in terms of medium-term averages and in terms of longer term trends.

 Another interesting feature is the direct relationship between the EPUI and VIX indices. Based on rolling correlations analysis (see chart below), the traditionally positive correlation between the two indices has broken down around the start of 2Q 2016 and since then all three measures of correlation - the 6-months, the 12-months and the 24-months rolling correlations - have trended to the downside, turning negative with the start of 2H 2016. Since November 2016, we have a four months period when all three correlations are in the negative territory, the first time this happened since June 2007 and only the second time this happened in history of both series (since January 1997). Worse, the previous episode of all three correlations being negative lasted only two months (June and July 2007), while the current episode is already 4 months long.

Final point worth making is that while volatility of VIX has collapsed both on trend and in level terms since the start of H1 2016 (see chart below), volatility in EPUI has shot up to historical highs.

Taken together, the three empirical observations identified above suggest that the current markets and economies are no longer consistent with increased traditional risk environment (environment of measurable and manageable risks), but instead represent VUCA (volatile, uncertain, complex and ambiguous) environment. The VUCA environment, by its nature, is characterised by low predictability of risks, with uncertainty and ambiguity driving down efficacy of traditional models for risk assessments and making less valid traditional tools for risk management. Things are getting increasingly more complex and uncertain, unpredictable and unmanageable.

16/9/2014: Mapping Uncertainty Across Industries

A very interesting post on HBR Blog (http://blogs.hbr.org/2014/09/the-industries-plagued-by-the-most-uncertainty/) mapping technological uncertainty against demand uncertainty across major industries.

Two charts:

11/9/2012: Inherent limit to artificial intelligence?

In a rather common departure from economics (as defined by rational expectations subset of the discipline) on this blog - here's a fascinating thinking about the artificial intelligence and the bounds of model-induced systems.

Especially close to me, as it explores that which I thought about back in 2003-2004 when I wrote an essay on the role of leaps of faith (irrational and discontinuous jumps in human creativity and thinking) as the foundation for humanity and, thus, a foundation for recognition of the property rights over uncertainty.

Economics 16/10/2010: Organizational systems and uncertainty

I came across this very interesting, and to me - far reaching - paper on the effects of organizational structures on the organization's ability to cope with uncertainty and change. Karynne L. Turner, Mona V. Makhija. “Measuring what you know: an individual information processing perspective” (April 15, 2010). Atlanta Competitive Advantage Conference 2010 Paper (here).

According to the information processing perspective, the organization’s ability to draw upon and utilize information is dependent on the relationship between structure and the ability of individuals to process information, facilitated by specific organizational aspects of the firm. The study considers the effect of two types of structure, organic (integrated or systemic) and mechanistic (siloed), on individuals’ ability to gather, interpret and synthesize information, and their problem-solving orientation. Evidence shows that individuals develop more information processing capability under organic than mechanistic structures, which in turn creates more problem solving orientation in individuals.

In short, the study lends support to the premise that better integrated, more diversified across skills and less siloed organizations produce more effective and efficient gathering, processing and interpreting of information, as well as better problem solving.


Effective management of knowledge is the basis of firms’ ability to compete (Zander and Kogut, 1995; Nonaka, 1994). This is achieved through organizational design (Teece at al., 1997) that underlies “the means by which firms acquire, disseminate, interpret and integrate organizational knowledge”.

Organizational structure embodies a number of key elements, such as control and coordination or management mechanisms, and human capital management that allocate tasks to work units and individuals, and coordinate them in a way that achieves organizational goals. The manner in which this is done is critical due to problems created by

• External uncertainty associated with suppliers, competitors and consumer demand (Gresov and Drazin, 1997; Sine, Mitsuhashi and Kirsch, 2006), or
• Internal uncertainty, due to the complexity of internal coordination, measurement difficulties and changing processes (Habib and Victor, 1991).
  

Uncertainty reduces the effectiveness of pre-established routines, technologies or goals, and increases the importance of problem solving (Becker and Baloff, 1969)). The more work related uncertainty increases, the greater the need there will be for information processing (Turner and Makhija, 2006 and Tushman, 1979).

One way in which an organization addresses uncertainty is by assigning specific responsibilities to specialized subunits, which collect, process and distribute information acting as “a set of nested systems” (Daft and Weick, 1984).

Literature distinguishes two types of organizational structures, mechanistic and organic. These structures differ in the distribution of tasks, the flow of information among individuals and across units, and the extent to which there is interaction with the environment (Shremata, 2000; Gibson and Birkinshaw, 2004).

Mechanistic forms of organization are characterized by hierarchical division of labor, in which communication tends to be in one direction – top to bottom. Individuals develop deep expertise in their own designated jobs, which tend to be clearly specified and specialized in individual knowledge. The mechanistic structures do not allow for much flexibility (Parthasarthy and Sethi, 1993).

Organic forms of organizations are based on horizontally-administered teams, in which all members participate in management decisions (Baum and Wally, 2003), allowing for worker autonomy, responsibilities adaptation. Team members developing competence across multiple tasks, thus diversifying their skills and knowledge sets. Individuals have broader unit-level knowledge rather than just one job and develop greater flexibility.

The structural differences between mechanistic and organic organizational forms are likely to influence the development of information processing capability in organizational members, reflected in organization’s ability to gather, interpret and synthesize information. Turner and Makhija (2010) consider the impact of different types of structures on each of these three aspects of organizational members’ information processing capability.

Turner and Makhija (2010) postulate a set of testable hypotheses all of which are confirmed:

H1: Organic structures lead to more gathering of information than mechanistic structures.
Implication: uncertainty is reduced in organic (integrated or more horizontal) structures through reduced information asymmetries vis-à-vis external environment.

H2: Organic structures lead to more similarly interpreted information than mechanistic structures.
Implication: information asymmetries are reduced across the broader range of the organization structures in the organic setting.

H3: Organic structures lead to more synthesized information than mechanistic structures.
Implication: organic systems are better capable of integrating information of various types.

H4: More gathering of information is associated with greater problem solving orientation.
Implication: organic systems are better able to cope with converting uncertainty into manageable risks systems.

H5: More similarly interpreted information is associated with greater problem solving orientation.
Implication: better information processing in organic systems results in better problem solving, so information is used more effectively.

H6: More synthesized knowledge is associated with greater problem solving orientation.
Implication: individuals also tended to synthesize, or understand the interrelationships among different types of information, much better than individuals working in mechanistic structures.

H7: Information processing capability mediates the relationship between organizational design and problem solving orientation
Implication: the effects of individuals’ information processing on their problem solving orientation is greater in the organic structures, reflecting their comfort with problem situations in their work.

Turner and Makhija (2010) research shows that, when operating in two different types of structures, individuals process information differently in all three respects: gathering, interpreting, and synthesizing information.

These findings have several far-reaching implications for the organizational structures found in Ireland.

Firstly, it is clear that hierarchical and fixed systems approach to public services provision – characterized by the lack of communications between vertically-integrated public sector departments and organizations leads to their inherently lower ability to absorb, process and implement informational processes that manage uncertainty.

Secondly, this shows why successful entrepreneurial ventures are horizontal in nature and less siloed.

Third, it shows that our political system – with disproportionate powers allocated to the executive, as opposed to more uniform distribution of powers between the executive, legislative and judiciary – is similarly to the public sector less equipped to handle uncertainty.